Multiclass SVM Model Selection Using Particle Swarm Optimization

Authors:
Bruno Feres de Souza;Andre C. P. L. F. de Carvalho;Rodrigo Calvo;Renato Porfirio Ishii
Affiliations:
University of Sao Paulo, Brazil;University of Sao Paulo, Brazil;University of Sao Paulo, Brazil;University of Sao Paulo, Brazil
Venue:
HIS '06 Proceedings of the Sixth International Conference on Hybrid Intelligent Systems
Year:
2006

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Abstract

Tuning SVM hyperparameters is an important step for achieving good classification performance. In the binary case, the model selection issue is well studied. For multiclass problems, it is harder to choose appropriate values for the base binary models of a decomposition scheme. In this paper, the authors employ Particle Swarm Optimization to perform a multiclass model selection, which optimizes the hyperparameters considering both local and globalmodels. Experiments conducted over 4 benchmark problems show promising results.